Conditional central algorithms for worst-case estimation and filtering

This paper deals with conditional central algorithms in a worst-case setting. The role and importance of these algorithms in identification and filtering is illustrated by showing that problems like ℋ₂ optimal identification and state filtering, in contexts where disturbances are described through norm bounds, are reducible to the computation of conditional central algorithms. The solution of the conditional Chebichev center problem is completely characterized for the case when energy norm bounded disturbances are considered. A closed form solution is obtained in terms of finding the unique real root of a polynomial equation